OKX - Vice President, AI Strategy & Transformation

• 1. Company AI Strategy & Transformation • Own the company’s AI roadmap end-to-end — from foundation model selection to business-unit-specific deployment plans: • Define and continuously refine the enterprise AI strategy, aligning it to revenue goals, product differentiation, and operational efficiency targets. • Conduct rigorous build-vs-buy-vs-partner analysis for foundation models, AI tooling, inference infrastructure, and data platforms. • Establish an AI governance framework covering model risk, data privacy, bias mitigation, and regulatory compliance across jurisdictions. • Serve as the primary AI advisor to the CEO and executive leadership team; translate frontier AI developments into actionable business implications. • Build and maintain a rolling 6/12/24-month AI transformation roadmap with clear milestones, investment thresholds, and go/no-go decision points. • Identify and evaluate strategic AI acquisition, investment, and partnership opportunities. • 2. System Building & Technical Execution • Architect and deliver production-grade AI systems that create measurable business impact — not just prototypes: • Lead the architecture of LLM-powered applications including RAG systems, agentic workflows, fine-tuning pipelines, and prompt engineering frameworks at enterprise scale. • Design and implement AI-native infrastructure: model serving, automated evaluation, A/B testing frameworks, version control for prompts and models, and continuous monitoring for quality and drift. • Build and optimize AI agent systems, multi-model orchestration, tool-use chains, and autonomous workflow engines that solve real business problems end-to-end. • Build robust evaluation and benchmarking systems for AI outputs — measuring hallucination rates, task completion accuracy, latency, safety, and end-user satisfaction. • Personally prototype and review critical AI system designs; maintain hands-on technical credibility with the engineering team. • 3. Organizational AI Enablement & Adoption • Drive AI adoption across every business unit — making AI a core competency for the entire organization, not just the engineering team: • Design and execute a company-wide AI literacy program segmented by role: executive leadership, product managers, engineers, operations, customer-facing teams, and support functions. • Create internal AI tooling, templates, and playbooks that make it radically easy for every business unit to leverage AI capabilities (prompt libraries, no-code/low-code AI interfaces, internal copilots, AI-assisted workflows). • Establish an AI Center of Excellence that serves as the hub for best practices, reusable components, and cross-functional AI project incubation. • Implement a structured AI use-case intake and prioritization process: partner with each business unit to identify high-ROI AI opportunities, scope them properly, and execute with embedded AI support. • Build an AI talent strategy: define hiring profiles for AI engineers and applied AI roles, design technical interview processes, and develop retention programs for top AI talent. • Foster a culture of responsible AI experimentation: psychological safety to try and fail fast, coupled with rigorous post-mortems and knowledge sharing across BUs. • What We Look For In You • 10+ years in AI / deep learning, with at least 5 years in a senior leadership role (Director+ or equivalent at a top-tier tech company, high-growth startup, or leading AI lab). • Demonstrated track record of shipping production AI systems that directly impacted business outcomes at scale (revenue, engagement, efficiency). • Deep expertise across the modern AI stack: large language models, transformer architectures, RAG, fine-tuning, RLHF/DPO, prompt engineering, and agentic AI frameworks. • Strong publication record, open-source contributions, or recognized thought leadership in the AI community (conference talks, technical blog posts, courses, or widely-adopted tools). • Exceptional communication skills: ability to present complex technical concepts to board-level audiences and translate business needs into technical specifications. • Advanced degree (MS or PhD) in Computer Science, Artificial Intelligence, Statistics, or a related quantitative field.